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Document Domain Randomization for Deep Learning Document Layout Extraction
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In: Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR, September 5--10, Lausanne, Switzerland) ; https://hal.inria.fr/hal-03336444 ; Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR, September 5--10, Lausanne, Switzerland), Sep 2021, Lausanne, Switzerland. pp.497-513, ⟨10.1007/978-3-030-86549-8_32⟩ (2021)
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Document Domain Randomization for Deep Learning Document Layout Extraction ...
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A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations ...
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Extractive Research Slide Generation Using Windowed Labeling Ranking ...
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Additional file 3 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models ...
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Additional file 3 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models ...
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Audio-visual Recognition of Overlapped speech for the LRS2 dataset ...
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Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets
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In: ISSN: 0884-8173 ; EISSN: 1098-111X ; International Journal of Intelligent Systems ; https://www.hal.inserm.fr/inserm-03026626 ; International Journal of Intelligent Systems, Wiley, 2019, 34 (6), pp.1261-1280. ⟨10.1002/int.22095⟩ (2019)
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Additional file 1: of Prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study ...
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Additional file 1: of Prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study ...
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Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets
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An interaction consensus in group decision making under distributed trust information
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A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust
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Tibetan Trisyllabic Light Verb Construction Recognition
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In: Zhao, Weina; Li, Lin; Liu, Huidan; & Wu, Jian. (2016). Tibetan Trisyllabic Light Verb Construction Recognition. Himalayan Linguistics, 15(1). doi:10.5070/H915130102. Retrieved from: http://www.escholarship.org/uc/item/2226c4k2 (2016)
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A Chinese to Tibetan Machine Translation System with Multiple Translating Strategies
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In: Liu, Huidan; Zhao, Weina; Yu, Xin; & Wu, Jian. (2016). A Chinese to Tibetan Machine Translation System with Multiple Translating Strategies. Himalayan Linguistics, 15(1). doi:10.5070/H915130103. Retrieved from: http://www.escholarship.org/uc/item/6kz2v0g3 (2016)
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Abstract:
This paper proposes a Chinese to Tibetan machine translation system with multiple translating strategies. The key corpora and technologies are explained in detail. Experiments show the sub systems output the translation of each phrase in the same order as they are in the Chinese sentence rather than in a Tibetan sentence, which leads to worse translation quality. So an order adjusting model is essential to Chinese to Tibetan translation system. The recall of translation phrase makes an improvement of 9.71% over the popular off-the-shelf language neutral statistical machine translation programme Moses. Our translation system achieves a speed ofabout 0.175s per sentence, which meets the requirement of the computer aided translation system.
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Keyword:
machine translation; NLP; Tibeta
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URL: http://www.escholarship.org/uc/item/6kz2v0g3
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Trust Based Consensus Model for Social Network in an Incomplete Linguistic Information Context
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Visual consensus feedback mechanism for group decision making with complementary linguistic preference relations
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Consistency based estimation of fuzzy linguistic preferences. The case of reciprocal intuitionistic fuzzy preference relations
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Automatic Identification of Research Articles from Crawled Documents
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In: Seventh International Conference on Web Search and Data Mining, February 24-28, 2014, New York City, New York. (2014)
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